Impact of Receivers Location on the Accuracy of Capsule Endoscope Localization

[EN] In recent years, localization for capsule endoscopy applications using Ultra-Wideband (UWB) technology has become an attractive field of study due to its potential benefits for patients. Performance analysis of RF-based localization techniques are very limited in literature. Most of the available studies rely on software simulations using digital human models. Nonetheless, no realistic studies based on in-vivo measurements has been reported yet. This paper investigates the performance of RSS-based technique for three-dimensional (3D) localization in the UWB frequency band. Impact of receivers selection as well as of the evaluated path loss model on the localization accuracy is investigated. Results obtained through CST-based simulations and from recently conducted in-vivo measurements are presented and compared.This work was supported by the European Union's H2020:MSCA:ITN program for the "Wireless In-body Environment Communication- WiBEC" project under the grant agreement no. 675353. This work was also funded by the Ministerio de Economia y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Barbi, M.; Garcia-Pardo, C.; Cardona Marcet, N.; Andrea Nevárez; Vicente Pons Beltrán; Frasson, M. (2018). Impact of Receivers Location on the Accuracy of Capsule Endoscope Localization. IEEE. 340-344. https://doi.org/10.1109/PIMRC.2018.8580862S34034

Localization for capsule endoscopy at UWB frequencies using an experimental multilayer phantom

[EN] Localization inside the human body using ultrawideband (UWB) wireless technology is gaining importance in several medical applications such as capsule endoscopy. Performance analysis of RF based localization techniques are mainly conducted through simulations using numerical human models or through experimental measurements using homogeneous phantoms. One of the most common implemented RF localization approaches uses the received signal strength (RSS). However, to the best of our knowledge, no experimental measurements employing multilayer phantoms are currently available in literature. This paper investigates the performance of RSS-based technique for two-dimensional (2D) localization by employing a two-layer experimental phantom-based setup. Preliminary results on the estimation of the in-body antenna coordinates show that RSS-based method can achieve a location accuracy on average of 0.5-1 cm within a certain range of distances between in-body and on-body antenna.This work was supported by the European Union’s H2020:MSCA:ITN program for the ”Wireless In-body Environment Communication- WiBEC” project under the grant agreement no. 675353. This work was also funded by the Programa de Ayudas de Investigación y Desarrollo (PAID-01-16) from Universitat Politècnica de València and by the Ministerio de Economía y Competitividad, Spain (TEC2014-60258-C2-1-R), by the European FEDER funds.Barbi, M.; Pérez Simbor, S.; García Pardo, C.; Andreu Estellés, C.; Cardona Marcet, N. (2018). Localization for capsule endoscopy at UWB frequencies using an experimental multilayer phantom. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/WCNCW.2018.8369015

Analysis of the Localization Error for Capsule Endoscopy Applications at UWB Frequencies

Localization for Wireless Capsule Endoscopy (WCE) in the Ultra-Wideband frequency band is a very active field of investigation due to its potential advantages in future endoscopy applications. Received Signal Strength (RSS) based localization is commonly preferred due to its simplicity. Previous studies on Ultra-Wideband (UWB) RSS-based localization showed that the localization accuracy depends on the average ranging error related to the selected combination of receivers, which not always is the one experiencing the highest level of received power. In this paper the tendency of the localization error is further investigated through supplementary software simulations and previously conducted laboratory measurements. Two-dimensional (2D) and three-dimensional (3D) positioning are performed and the trend of the localization error compared in both cases. Results shows that the distribution of the selected path loss values, corresponding to the receivers used for localization, around the in-body position to estimate also affects the localization accuracy.This work was supported by the H2020:MSCA:ITN program for the “Wireless In-body Environment Communication- WiBEC” project under the grant agreement no. 675353. This work was also supported by the European Union’s H2020:MSCA:ITN program for the ”mmWave Communications in the Built Environments - WaveComBE” project under the grant agreement no. 766231.Barbi, M.; Pérez-Simbor, S.; Garcia-Pardo, C.; Cardona Marcet, N. (2019). Analysis of the Localization Error for Capsule Endoscopy Applications at UWB Frequencies. IEEE. https://doi.org/10.1109/ISMICT.2019.8743813

UWB RSS-based Localization for Capsule Endoscopy using a Multilayer Phantom and In Vivo Measurements

[EN] In recent years, the localization for capsule endoscopy applications using ultrawideband (UWB) technology has become an attractive field of investigation due to its potential benefits for patients. The literature concerning performance analysis of radio frequency-based localization techniques for in-body applications at UWB frequencies is very limited. Available studies mainly rely on finite-difference time-domain simulations, using digital human models and on experimental measurements by means of homogeneous phantoms. Nevertheless, no realistic analysis based on multilayer phantom measurements or through in vivo experiment has been reported yet. This paper investigates the performance of the received signal strength-based approach for 2-D and 3-D localizations in the UWB frequency band. For 2-D localization, experimental laboratory measurements using a two-layer phantom-based setup have been conducted. For 3-D localization, data from a recently conducted in vivo experiment have been used. Localization accuracy using path loss models, under ideal and non-ideal channel estimation assumptions, is compared. Results show that, under nonideal channel assumption, the relative localization error slightly increases for the 2-D case but not for the in vivo 3-D case. Impact of receivers selection on the localization accuracy has also been investigated for both 2-D and 3-D cases.This work was supported in part by the European Union's H2020 through the MSCA: ITN Program "Wireless in-Body Environment Communication-WiBEC" under Grant 675353, in part by the Programa de Ayudas de Investigacion y Desarrollo, Universitat Politecnica de Valencia under Grant PAID-01-16, and in part by the Ministerio de Economia y Competitividad, Spain, through the European FEDER Funds under Grant TEC2014-60258-C2-1-R.Barbi, M.; Garcia-Pardo, C.; Nevárez, A.; Pons Beltrán, V.; Cardona Marcet, N. (2019). UWB RSS-based Localization for Capsule Endoscopy using a Multilayer Phantom and In Vivo Measurements. IEEE Transactions on Antennas and Propagation. 67(8):5035-5043. https://doi.org/10.1109/TAP.2019.2916629S5035504367

Anatomical Classification of the Gastrointestinal Tract Using Ensemble Transfer Learning

Endoscopy is a procedure used to visualize disorders of the gastrointestinal (GI) lumen. GI disorders can occur without symptoms, which is why gastroenterologists often recommend routine examinations of the GI tract. It allows a doctor to directly visualize the inside of the GI tract and identify the cause of symptoms, reducing the need for exploratory surgery or other invasive procedures. It can also detect the early stages of GI disorders, such as cancer, enabling prompt treatment that can improve outcomes. Endoscopic examinations generate significant numbers of GI images. Because of this vast amount of endoscopic image data, relying solely on human interpretation can be problematic. Artificial intelligence is gaining popularity in clinical medicine. Assist in medical image analysis and early detection of diseases, help with personalized treatment planning by analyzing a patient’s medical history and genomic data, and be used by surgical robots to improve precision and reduce invasiveness. It enables automated diagnosis, provides physicians with assistance, and may improve performance. One of the significant challenges is defining the specific anatomic locations of GI tract abnormalities. Clinicians can then determine appropriate treatment options, reducing the need for repetitive endoscopy. Due to the difficulty of collecting annotated data, very limited research has been conducted on the localization of anatomical locations by classification of endoscopy images. In this study, we present a classification of GI tract anatomical localization based on transfer learning and ensemble learning. Our approach involves the use of an autoencoder and the Xception model. The autoencoder was initially trained on thousands of unlabeled images, and the encoder then separated and used as a feature extractor. The Xception model was also used as a second model to extract features from the input images. The extracted feature vectors were then concatenated and fed into a Convolutional Neural Network for classification. This combination of models provides a powerful and versatile solution for image classification. By using the encoder as a feature extractor that can transfer the learned knowledge, it is possible to improve learning by allowing the model to focus on more relevant and useful data, which is extremely valuable when there are not enough appropriately labelled data. On the other hand, the Xception model provides additional feature extraction capabilities. Sometimes, one classifier is not enough in machine learning, as it depends on the problem we are trying to solve and the quality and quantity of data available. With ensemble learning, multiple learning networks can work together to create a stronger classifier. The final classification results are obtained by combining the information from both models through the CNN model. This approach demonstrates the potential for combining multiple models to improve the accuracy of image classification tasks in the medical domain. The HyperKvasir dataset is the main dataset used in this study. It contains 4,104 labelled and 99,417 unlabeled images taken at six different locations in the GI tract, including the cecum, ileum, pylorus, rectum, stomach, and Z line. After dataset preprocessing, which includes noise deduction and similarity removal, 871 labelled images remained for the purpose of this study. Our method was more accurate than state-of-the-art studies and had a higher F1 score while categorizing the input images into six different anatomical locations with less than a thousand labelled images. According to the results, feature extraction and ensemble learning increase accuracy by 5%, and a comparison with existing methods using the same dataset indicate improved performance and reduced cross entropy loss. The proposed method can therefore be used in the classification of endoscopy images

A hybrid localization method for Wireless Capsule Endoscopy (WCE)

Wireless capsule endoscopy (WCE) is a well-established diagnostic tool for visualizing the Gastrointestinal (GI) tract. WCE provides a unique view of the GI system with minimum discomfort for patients. Doctors can determine the type and severity of abnormality by analyzing the taken images. Early diagnosis helps them act and treat the disease in its earlier stages. However, the location information is missing in the frames. Pictures labeled by their location assist doctors in prescribing suitable medicines. The disease progress can be monitored, and the best treatment can be advised for the patients. Furthermore, at the time of surgery, it indicates the correct position for operation. Several attempts have been performed to localize the WCE accurately. Methods such as Radio frequency (RF), magnetic, image processing, ultrasound, and radiative imaging techniques have been investigated. Each one has its strengths and weaknesses. RF-based and magnetic-based localization methods need an external reference point, such as a belt or box around the patient, which limits their activities and causes discomfort. Computing the location solely based on an external reference could not distinguish between GI motion from capsule motion. Hence, this relative motion causes errors in position estimation. The GI system can move inside the body, while the capsule is stationary inside the intestine. This proposal presents two pose fusion methods, Method 1 and Method 2, that compensate for the relative motion of the GI tract with respect to the body. Method 1 is based on the data fusion from the Inertial measurement unit (IMU) sensor and side wall cameras. The IMU sensor consists of 9 Degree-Of-Freedom (DOF), including a gyroscope, an accelerometer, and a magnetometer to monitor the capsule’s orientation and its heading vector (the heading vector is a three-dimensional vector pointing to the direction of the capsule's head). Four monochromic cameras are placed at the side of the capsule to measure the displacement. The proposed method computes the heading vector using IMU data. Then, the heading vector is fused with displacements to estimate the 3D trajectory. This method has high accuracy in the short term. Meanwhile, due to the accumulation of errors from side wall cameras, the estimated trajectory tends to drift over time. Method 2 was developed to resolve the drifting issue while keeping the same positioning error. The capsule is equipped with four side wall cameras and a magnet. Magnetic localization acquires the capsule’s global position using 9 three-axis Hall effect sensors. However, magnetic localization alone cannot distinguish between the capsule’s and GI tract’s motions. To overcome this issue and increase tracking accuracy, side wall cameras are utilized, which are responsible for measuring the capsule’s movement, not the involuntary motion of the GI system. A complete setup is designed to test the capsule and perform the experiments. The results show that Method 2 has an average position error of only 3.5 mm and can compensate for the GI tract’s relative movements. Furthermore, environmental parameters such as magnetic interference and the nonhomogeneous structure of the GI tract have little influence on our system compared to the available magnetic localization methods. The experiment showed that Method 2 is suitable for localizing the WCE inside the body